Summary Human
Manuela Benary / Mohammad El-Ismail
CUBI / CCCC2023-03-31
#Quality control metrices
The figures are showing different quality metrices for the control samples.
| Sample_Tag | nCells |
|---|---|
| Control | 16260 |
The figures are showing different quality metrices for the control samples.
The figures are showing different quality metrices for the control samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the control samples.
## Warning: Transformation introduced infinite values in continuous x-axis## Warning: Removed 22 rows containing non-finite values (`stat_density()`).
The figures are showing different quality metrices for the control samples.
The figures are showing different quality metrices for the copanlisib samples.
| Sample_Tag | nCells |
|---|---|
| Copanlisib | 2591 |
The figures are showing different quality metrices for the copanlisib samples.
The figures are showing different quality metrices for the copanlisib samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the copanlisib samples.
The figures are showing different quality metrices for the copanlisib samples.
The figures are showing different quality metrices for the alpelisib samples.
| Sample_Tag | nCells |
|---|---|
| Alpelisib | 17198 |
The figures are showing different quality metrices for the alpelisib samples.
The figures are showing different quality metrices for the alpelisib samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the alpelisib samples.
## Warning: Transformation introduced infinite values in continuous x-axis## Warning: Removed 9 rows containing non-finite values (`stat_density()`).
The figures are showing different quality metrices for the alpelisib samples.
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
#Investigation of unwanted variations - cell cycle phases and mitochondrial gene expression
## Warning: The following features are not present in the object: RAD51, not
## searching for symbol synonyms## Centering and scaling data matrix## PC_ 1
## Positive: RETNLB, IL10, CPA3, SPRR3, AL355922.1, UPK1B, NKG7, IVL, HBQ1, TPSG1
## TPSD1, HBG1, OR6M1, ALAS2, KCCAT333, HBB, SPRR2A, NTS, WIF1, PCP4L1
## HBA2, LYPD2, ESM1, AGTR2, HBA1, OR51E1, CALCA, TNNC2, MT3, GPHA2
## Negative: RPS19, RPL11, RPLP1, RPS8, RPS12, RACK1, RPS13, RPS20, RPL37A, RPL27A
## RPS11, RPS24, TMSB10, NAP1L1, RPL15, RPS3, RPS6, RPL14, SEC61G, RPL5
## CALU, RPL13, ATP5F1E, LGALS1, RPS28, RPS16, RPL18, CD63, RPL35A, AC016739.1
## PC_ 2
## Positive: RPS15A, PERP, RPL3, RPL26P19, SNHG5, HMGN1, RPL6, RPL21, SNHG6, LRRC75A-AS1
## RPL9, C1orf43, RPL22, RPL7A, RPL13A, AC024293.1, GGCT, RPL7L1, AC008026.1, ZFAS1
## CSTB, RPL7P9, COX6C, DSG2, SRP9, ADI1, EPCAM, GAS5, TFAP2A, COMMD6
## Negative: SPARC, COL1A2, COL3A1, COL1A1, COL5A2, DCN, FBN1, SERPING1, PCOLCE, COL5A1
## FKBP7, PAM, AEBP1, CREB3L1, NFIX, BMP1, SERPINF1, CCDC80, OLFML2B, FSTL1
## PDGFRA, TGFBR3, IGFBP7, FGFR1, EBF1, TGFBR2, CYR61, TUBA1A, CDH11, C1R
## PC_ 3
## Positive: SAA1, CTSS, SAA3P, PFN1, F13A1, ARG1, SAA2, LYZ, BACH1-IT3, BEND3
## AC012618.1, AIF1, ACTG2, CTSC, ECM1, RPS15AP24, RPS26P52, RARS2, FABP5, PRDX1
## RPL35P4, CD72, AL162430.1, PF4V1, AL355802.1, C1QB, SEC61G, FABP5P7, MAF, C1QA
## Negative: MTND3P17, COL3A1, COL5A2, CTHRC1, LRRC75A-AS1, COL1A1, FNDC1, SNHG5, SDC2, COL1A2
## THBS2, PRAME, SPARC, ASPN, RPS15A, HOOK1, COL12A1, GPC3, RPL6, ITM2A
## SNHG6, LUM, PRRX1, FSTL1, PERP, EN1, GGCT, UCHL1, RPL26P19, GAS5
## PC_ 4
## Positive: COL12A1, LIMA1, SERPINH1, TIMP3, MTND3P17, THBS2, SPATS2L, CAST, KRT6A, CAMK2N1
## IFI44, SLC16A2, IFI27, BST2, COL1A1, KCNMA1, FSTL1, PLS3, TNFSF10, SEMA3C
## KLK5, TPM1, GBP1, SAMD9, FHL2, OAS2, OAS3, F3, S100A2, RAB31
## Negative: RPS12, RPL37A, RPS8, RPS24, HLA-DRB1, RPS11, RPS19, HLA-DPB1, PRAME, RPL37
## RPS13, RPL27A, HLA-DQB1, CD74, AC008481.1, RPL31, CXCR4, RPL11, RPS7, RPS18
## RACK1, RPL12, HLA-DRB5, HLA-DRA, HLA-DRB6, HLA-DPA1, UCHL1, HLA-DQA1, LRRC75A-AS1, RPS27A
## PC_ 5
## Positive: EFEMP1, TNXB, C1R, DCN, ACKR3, FCRL2, LGI2, OGN, C3, PLPP3
## ADAMTS5, COL14A1, CADM3, LBP, TGFBR2, NOVA1, GSN, CYGB, SLC4A4, TMEFF2
## HTRA3, THBS3, CXCL12, C1S, TGFBR3, AC011352.1, MT2P1, KLF4, LAMA2, MT2A
## Negative: COL12A1, NCAM1, CDH11, CALD1, COL8A1, PMEPA1, THBS2, TPM2, HS6ST2, ACTA2
## AC116917.1, CTGF, POSTN, TNC, GNG11, COL6A3, COL5A2, IGFBP7, LBH, SEMA7A
## CNN3, BMP1, THBS1, TPM1, OLFML2B, LOXL3, IGFBP3, COL5A1, TAGLN, ACTN1
## 18:17:49 UMAP embedding parameters a = 0.9922 b = 1.112## 18:17:49 Read 25592 rows and found 40 numeric columns## 18:17:49 Using Annoy for neighbor search, n_neighbors = 30## 18:17:49 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 18:17:54 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c63948c63830
## 18:17:54 Searching Annoy index using 1 thread, search_k = 3000
## 18:18:03 Annoy recall = 100%
## 18:18:04 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:18:06 Initializing from normalized Laplacian + noise (using irlba)
## 18:18:07 Commencing optimization for 200 epochs, with 1289790 positive edges
## 18:18:22 Optimization finished
#Integration
#Clustering analysis
#Celltype identification singleR
#Label comparison
#Celltype identification puram
#Transcript analysis
## Rows: 34609 Columns: 4
## ── Column specification ───────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Cell_Index, Labels
## dbl (2): UMIs_human, UMIs_mouse
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Celltype identification demultiplexed
## 18:20:49 UMAP embedding parameters a = 0.9922 b = 1.112## 18:20:49 Read 25592 rows and found 30 numeric columns## 18:20:49 Using Annoy for neighbor search, n_neighbors = 30## 18:20:49 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 18:20:53 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c639406d6764
## 18:20:53 Searching Annoy index using 1 thread, search_k = 3000
## 18:21:01 Annoy recall = 100%
## 18:21:02 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:21:04 Initializing from normalized Laplacian + noise (using irlba)
## 18:21:06 Commencing optimization for 200 epochs, with 1140710 positive edges
## 18:21:21 Optimization finished
## Labels
## Xenograft mainly_human mainly_mouse unique_human
## HN10621 76 3560 628
## HN10960 0 160 32
## HN11097_Control 4 1318 172
## HN15239A_Alpelisib 106 7351 1815
## HN15239A_Control 29 10127 214## Labels
## Xenograft mainly_human mainly_mouse unique_human
## HN10621 0.0029696780 0.1391059706 0.0245389184
## HN10960 0.0000000000 0.0062519537 0.0012503907
## HN11097_Control 0.0001562988 0.0515004689 0.0067208503
## HN15239A_Alpelisib 0.0041419193 0.2872381994 0.0709206002
## HN15239A_Control 0.0011331666 0.3957095967 0.0083619881